Hepatocurative Properties of Eucalyptus camaldulensis Stem Bark Extract

: The research investigates the hepatocurative potentials of Eucalyptus camaldulensis solvents extract on CCl 4 induced liver damage using albino rats, partial characterization of the most active extract was done using GCMS techniques. Forty-two rats were placed into seven groups of six rats each. Group I and II were normal control and test control respectively. Groups III to VII were induced with liver damage using 120mg/kg of CCl 4 and administered with standard drug, n-hexane extract (E1), chloroform extract (E2), ethyl acetate extract (E) and methanol extract (E4) respectively. 24 hours after CCl 4 administration, a significant (p<0.05) increase in mean serum level activities of AST, ALT, ALP with decrease in conjugated bilirubin and albumin concentration was recorded in test control group compared to normal control. This was reversed upon administration chloroform and methanol extract with former been most active. Antioxidant analysis reveals a significant (p<0.05) decrease in level of liver tissue antioxidant enzymes (SOD, CAT and GPx) with concomitant increase in MDA of test control compared to normal control rats. After extracts administration, increase in the antioxidants enzymes (SOD, Catalase and GPx) levels was recorded in methanol and standard drug administered groups.

INTRODUCTION
Eucalyptus camaldulensis usually called "red river gum" is a tree of the genus Eucalyptus. It is a plantation species in many parts of the world which is native of Austria where it is widespread especially beside inland water courses (Hill, 2019). Eucalyptus camaldulensis typically grows to a height of 20 meters (66 ft) but sometimes to 45 meters (148 ft) and often does not develop a lignotuber. The bark is smooth white or cream-colored with patches of yellow, pink or brown. There are often loose, rough slabs of bark near the base. The juvenile leaves are lance-shaped, 80-180 mm (3.1-7.1 in) long and 13-25 mm (0.51-0.98 in) wide. Adult leaves are lance-shaped to curved, the same dull green or greyish green color on both sides, 50-300 mm (2.0-11.8 in) long and 7-32 mm (0.28-1.26 in) wide on a petiole 8-33 mm (0.31-1.30 in) long. The flower buds are arranged in groups of seven, nine or sometimes eleven, in leaf axils on a peduncle 5-28 mm (0.20-1.10 in) long Flowering mainly occurs in summer with white flower (Brooker and Slee, 2019).
The plant has many medicinal applications especially as a cough remedy and expectorant, it is also used as febrifuge, tonic, astringent, antiseptic and homeostatic properties (Kerharo & Adam, 1974). In Senegal a leaf decoction sweetened with sugar is used to treat stomach-ache and dysmenorrhoea. In Sudan fresh leaves are applied against rheumatism, and the smoke of burnt leaves is inhaled for the treatment of respiratory problems. The gum is used medicinally to treat diarrhea and pharyngeal inflammations, and as an astringent. In Nigeria chewing sticks are obtained from the tree. The smoke of burnt leaves serves to repel insects (Ellis and Pfeiffer, 1990). An infusion of the bark is used as an eye drop for some eyes, ophthalmia and is highly effective in treating diarrhea (Boily and Vanpuyvelde, 1986).
Liver is the largest and heaviest internal organ of the body with a weight of 1 to 1.5 kg and representing 1.5% to 2.5% of the lean body mass. It is a reddish wedge shaped and covered by a network of connective tissue called Glisson capsule. It is located in the right upper quadrant of the abdominal cavity, resting just below the diaphragm and lies to the upper right side of the stomach and overlies the gallbladder. (Guyton and Hall, 1996). The liver regulate the flow of nutrients to the rest of the body as it controls the release of absorbed materials in to the systemic circulation and has a central role in carbohydrate, proteins and fat metabolism. The liver store substances, such as minerals, blood and vitamins, which can be released when required (Finlayson et al., 1995).
The liver metabolizes endogenous substances such as bilirubin and exogenous substances such as drugs, which are then excreted via kidney or the biliary system. It is the biggest reticulo-endothelial organ in the body and as such it has important immune functions in maintaining body's integrity. The basic functional units of the liver is the liver lobule. The human liver contains 50,000 to 100,000 individual lobules (Guyton and Hall, 1996). It is unique among the body's organs in that it can regenerate back cells that have been destroyed by some short term injury or disease. The liver can lose about 70 percent of its tissue to disease without ceasing to function.
Liver diseases have become one of the serious health problems and a major cause of death all over the world. Nearly 20,000 deaths and 250,000 new cases have been reported every year. Consequently, there is need to provide scientific basis for the use of plants such as Eucalyptus Camaldulensis in traditional medicine. It is against this background that this research was initiated in other to know the hepatocurative efficacy and bioactive compounds from Eucalyptus Camaldulensis stem bark.

Equipment and Apparatus
Standard equipment and apparatus were used in the study, some of them are: Spectrophotometer, Centrifuge, Water bath, Refrigerator, weighing balance, Micropipette, measuring cylinder, Beakers, Centrifuge tubes, Pasteur pipettes, Test tubes, Test tube racks, Hand gloves, Razor Blades, masking tapes.

Chemicals and Reagents
Some of the chemicals and reagent used include: Hexane, Chloroform, Ethyl acetate, Ammonia thocyanate Sodium hydroxide, Alanine amino transferase kit, Aspartate amino transferase kit, Alkaline phosphatase kit, Total protein kit, Albumin kit, Total and direct bilirubin kit, Superoxide dismutase kit, catalase kit, glutathione peroxidase kit and Charcoal.

Study Animals
Male and female albino rats weighing between 100 g to 150g were purchased from animal house of Biochemistry Department; Yobe State University Damaturu. The animals were housed in well-ventilated aluminium cages and had access to food and clean water ad libitum. The rats were allowed to acclimatize for one week prior to commence of experiment. Principle of laboratory animal care and ethical guidelines for investigation of experimental pain in conscious animals were observed during experimentation (NIH. 1996;Zimmermann, 1983).

Collection Identification and extraction of the plant material
Eucalyptus camaldulensis stem barks were collected from University farm of Yobe State University, Damaturu. The plant was identified and authenticated by a taxonomist at the Plant Biology Department of the same institution, shade dried and ground to powder. Five hundred grams of the powder was successfully extracted using different solvents of increasing polarity viz-a-viz hexane, chloroform, ethyl acetate and water with the aid of soxhlet extractor. The filtrate thus obtained was concentrated by complete evaporation of the solvent to yield solvents free extracts labelled E1, E2, E3 and E4 for Hexane, Chloroform, Ethyl acetate and aqueous extracts respectively. Extract were stored in a clean airtight plastic container at room temperature until use.
Twenty (10) gram of each extract was accurately weighed and dissolve in 100 ml of DMSO or distilled water to prepare a concentration of 100mg/ml. The volume of the extract for administration into the laboratory rats was determined based on the weight of the animals using the following relationship according to Muhammad et al., (2015).

Induction of liver damage
Liver damage was induced using Carbon tetrachloride (CCl 4 ) according to the method of Alhassan et al (2009) using 120 mg/kg of CCl 4 . A stock solution of CCl 4 was prepared in 1:1 by dissolving 25cm 3 of CCl 4 in 25cm 3 pure olive oil. It was as administered intraperitoneally. The volume of CCl 4 administered was determined by the weight of the rat according to the following relationship:

Evaluation of the Effect of Eucalyptus camaldulensis stem bark extract on the CCl 4 induced Liver Damage
Forty-two (42) experimental rats were placed into seven groups of six rats each. Groups II to VII were induced with liver damage using 120mg/kg of CCl 4. Group I: Normal Control. Group II: Test Control; induced with liver damage, no extract given Group III: Administered with standard Group IV: Administered with 150mg/kg body weight of n-hexane extract (E1) Group V: Administered with 150mg/kg body weight of chloroform extract (E2) Group VI: Administered with 150mg/kg body weight of ethyl acetate extract (E) Group VII: Administered with 150mg/kg body weight of methanol extract (E4) The rats in groups I and II were sacrificed 24 hours after inducement with CCl 4 and blood samples were collected to confirm inducement of liver damage using 120mg/kg of CCl 4 . The rats each from Group III, IV, V, VI and VII were sacrificed after two weeks oral administration of stem bark extracts of Eucalyptus camaldulensis and blood samples were collected for determination of liver functions indices i.e: Aspartate aminotransferase (AST) and Alanine Aminotransferases Assay (ALT) were assayed using Reitman and Frankel (1957) method, Alkaline Phosphatase (ALP) activity assayed using the method developed by Roy (1970), Bilirubin by method of Malloy and Evolyn (1937), and Total protein was determined using Biuret method by Tietz (1995). Anti-oxidants indices were assayed using sandwich-ELISA kits

Statistical Analysis
Results were expressed as mean ± standard deviation and analysed using ANOVA, with p value <0.05 considered significant, a component of GraphPad Instat3 Software version 3.05 by GraphPadInc was used for the analysis Figure 1 presents the effect of administration solvents extract of Eucalyptus camaldulensis stem bark extract on CCL 4 induced liver damage rats. A significant increase in mean serum level activities of AST, ALT, ALP with decrease in conjugated bilirubin and albumin was observed in test control rats compared to normal control. Upon administration of the extract, significant decrease in serum concentration of AST, ALT, ALP, was recorded in methanol extract administered group compared to the test control but still higher than the normal control group and standard drug administered group.

Fig 1: Serum Liver Function Indices of CCl 4 Induced Liver Damage Rats after two weeks of Oral Administration of solvents Extract of Eucalyptus camaldulensis Stem Bark
The effect on extracts administration on antioxidant indices (SOD, CAT, GPx and MDA) was presented in Table 1. A significant (p<0.05) decrease in level of SOD, Catalase and GPx with concomitant increase in MDA was observed in liver tissue of test control compared to normal control rats. After extracts administration, increase in the antioxidants enzymes (SOD, Catalase and GPx) levels was recorded in methanol and standard drug administered groups.  Figure 1 shows the GCMS chromatogram of most active methanol fraction, analysis of the spectra shows the presence of 2,4-bis(1,1-dimethylethyl) Phenol; Phthalic acid (5-methylhex-2-yl heptadecyl ester); Stearic acid 3-(octadecyloxy)propyl ester; and αamyrin.

DISCUSSION
Liver damage is a serious disease characterized by disturbances in the normal functions of the liver. It is clinically diagnosed by determining the serum concentration of liver enzymes (ALT, AST and ALP), bilirubin, total protein and albumin (Willett et al., 2004). ALT and AST are non-plasma specific enzymes involved in transamination of aspartic acid and alanine respectively, the enzymes were reported to reach higher than normal levels in the blood when there is necrosis of the parenchymal cells of the liver as in viral or toxic hepatitis (Sule, 2004). ALP is also a non-plasma specific enzyme involved in the hydrolysis of a variety of phosphate esters at alkaline pH. These enzymes were reported to reach higher than normal level in the blood in events of impaired liver function (Price and Stevens, 2003). Thus, they are used as serum markers of hepatic damage. Liver is the sole organ that produces albumin, despite the long life span of albumin, serum level of albumin is a good index of chronic liver disease (Alhassan et al., 2009). A significant increase in serum activities of ALT, AST and ALP (p<0.05) in CCl 4 induced control group compared to the normal control shows an indication of successful induction of liver damage using 120mg of CCl 4 . The enzymes were reported to be higher than normal levels in the blood when there is necrosis of the parenchymal cells of the liver as in viral or toxic hepatitis (Sule, 2004;Abubakar et al., 2009). Non-significant increase in albumin and conjugated bilirubin in hepatotoxic control rats might be due to onset the liver damage which trigger the released of newly synthesized albumin and conjugated bilirubin into the blood stream as a result of liver damage from CCl 4 administration (Muhammad et al., 2015).
Administration of solvent extracts from sequential partitioning led to a significant decrease (p<0.05) in the mean serum levels of liver enzymes, with a slight increase in total protein in group administered with ethyl acetate extract. This could be due to the continued release of newly synthesized protein/albumin by the damaged hepatocytes. This finding is in accordance with the findings of Muhammad et al., (2015) who reported a lower activities of transaminases in the liver of diabetes induced rats administered aqueous extract of K. senegalensis. Ali et al., (2011) also reported a significant decrease in the activity of liver enzymes in albino rats induced with liver damage after been treated with aqueous extract of K. senegalensis stem bark at doses of 250 mg/kg and 500 mg/kg body weight for 5 days.
The observed decrease in MDA and increase in level of SOD, GSHpx and CAT in hepatotoxic rats after administration with ethyl acetate extract shows that the extract possess some anti-oxidant principles and could scavenge excess free radicals generated by CCl 4 . The toxicity of carbon tetrachloride is achieved through metabolic activation by cytochrome P-450 enzymes, catalyzing the addition of an electron, which then allows homolytic cleavage and the loss of a chloride ion and the formation of the trichloromethyl radical that attack lipids and protein of the endoplasmic reticulum of the liver (Dai and Cederbaum, 1995).
The exact antioxidant mechanism(s) by which the extract cures liver damage is yet to be established. However, it can be postulated that it may act through its antioxidant potentials, since amyrin was reported have antioxidant activity, it may help in restoring the damaged liver tissue (Zhou et al., 2016). It could act at molecular level by inducing the expression of messenger RNA for these enzymes or by acting as a natural antioxidant (Nour-Eldine et al., 2016)


Human commensal Candida albicans strains demonstrate substantial within-host diversity and retained pathogenic potential

Candida albicans is a frequent colonizer of human mucosal surfaces as well as an opportunistic pathogen. C. albicans is remarkably versatile in its ability to colonize diverse host sites with differences in oxygen and nutrient availability, pH, immune responses, and resident microbes, among other cues. It is unclear how the genetic background of a commensal colonizing population can influence the shift to pathogenicity. Therefore, we undertook an examination of commensal isolates from healthy donors with a goal of identifying site-specific phenotypic adaptation and genetic variation associated with these phenotypes. We demonstrate that healthy people are reservoirs for genotypically and phenotypically diverse C. albicans strains, and that this genetic diversity includes both SNVs and structural rearrangements. Using limited diversity exploitation, we identified a single nucleotide change in the uncharacterized ZMS1 transcription factor that was sufficient to drive hyper invasion into agar. However, our commensal strains retained the capacity to cause disease in systemic models of infection, including outcompeting the SC5314 reference strain during systemic competition assays. This study provides a global view of commensal strain variation and within-host strain diversity of C. albicans and suggests that selection for commensalism in humans does not result in a fitness cost for invasive disease.

isolate under three carbon sources at 30°C for 24 hours in biological duplicate. Rate and carrying capacity were determined using the GrowthcurveR analysis number. We noted that the isolates globally exhibited copy number expansion in the 3 2 5 telomeric regions, and that copy number increases were more common on smaller 3 2 6 chromosomes. Overall, the considerable copy number variation among our commensal 3 2 7 isolates is in line with past work suggesting that host environmental pressures induce  Notably, we were able to identify structural rearrangements and putative 3 3 1 chromosome fusions, and used PCR to test for the presence of the fusion event. In 3 3 2 strain 880-2, we observed a fusion event between chromosomes 3 and 4, connected by 3 3 3 1 5 a 1.3 kb intervening sequence ( Fig 3C). This intervening sequence had 94% sequence 3 3 4 identity to an intergenic sub-telomeric region of chromosome 6. To determine whether 3 3 5 this was a true event or a sequencing artifact, we designed primers to span the junction 3 3 6 and performed PCR to amplify the fusion (Fig 3D). Using this approach, we observed 3 3 7 that in strain 880-2, there is a bona fide structural rearrangement that links 3 3 8 chromosomes 3 and 4. In strain 859-2, we observed a fusion event between 3 3 9 chromosomes 1 and 3, connected by an approximately 7kb intervening sequence with 3 4 0 no obvious sequence identity to the SC5314 reference strain, but instead had 99.76% 3C). We were again able to use PCR to span both junctions observed the presence of 3 4 3 the fusion between chromosomes 1, 2, and 3 ( Fig 3D). This fusion event may 3 4 4 correspond to the additional chromosomal band between chromosomes 2 and 3 that we were present in the SC5314 reference strain, indicating that the fusions were unique to 3 4 7 the specific isolate ( Fig 3D). These structural variations were not captured in the SNV  isolates, which suggests that even within similar strains, there is potential for structural  The set of isolates for sequencing were initially chosen based on variation in 3 5 8 growth rate in rich medium and alterations in invasion into agar. However, we 3 5 9 hypothesized that we may identify specific adaptations in C. albicans strains isolated 3 6 0 from different sites that allow for colonizing different host microenvironments. The host 3 6 1 sites commonly colonized by C. albicans vary dramatically in environmental cues, such 3 6 2 as nutrient availability, pH, immune responses, and resident microbiomes. Additionally, 3 6 3 we hypothesized we may identify phenotypes associated with specific C. albicans 3 6 4 clades, as we were able to identify structural variants shared between closely related isolates. To test this, we performed a set of growth analyses under multiple 3 6 6 environmental conditions, including pH stresses, nutrient limitation, cell wall stressors, 3 6 7 and antifungal drugs ( Fig 4A). These analyses produced a dense array of quantitative 3 6 8 phenotypic information for each strain. A) Growth curve analysis under multiple environmental conditions. Carrying 3 7 2 capacity (K) was normalized to SC5314, and the fold-change plotted by heatmap.

7 3
Aggregating strains (882-60, 882-46, and 811-7) demonstrate a consistently   Representative filamentation score of 0 (left). Representative filamentation score isolates for macrophage filamentation scoring, phagocytosis rates, and cell death 3 8 0 rates. SC5314 reference strain is indicated in red. For phagocytosis rates, 3 8 1 significant differences from the SC5314 reference strain were determined by 3 8 2 one-way ANOVA, with Dunnett's multiple correction testing. For cell death, 3 8 3 significant differences from the mock condition were determined by one-way 3 8 4 ANOVA, with Dunnett's multiple correction testing. Asterisks indicate P < 0.05 (*), 3 8 5 P < 0.005 (**), P < 0.001 (***) and P < 0.0001 (****). From these data, we identified 3 strains, 882-60, 882-46, and 811-7, that 3 8 8 consistently grew more poorly than the wildtype under multiple conditions; these strains 3 8 9 were those that exhibited aggregation at 30°C and slow growth in rich media conditions. These strains all belonged to Clade C and were closely related, despite arising from two 3 9 1 donors ( Fig 4A). Growth rates in the nutrient limitation conditions were generally 3 9 2 correlated with each other. However, we did not observe a correlation between body 3 9 3 site and growth rate, even in response to cues that would appear to be specific for a 3 9 4 particular body site, such as anaerobic growth. Across the commensal isolates, we 3 9 5 noted the most variation in growth in response to caffeine and the antifungals 3 9 6 fluconazole and rapamycin. In addition to growth, we measured each of the strains for  as we did not observe growth enrichment in cues specific to isolation sites.

0 4
A major stress condition and environmental factor impacting C. albicans in the 4 0 5 host is the immune response. Therefore, we moved from pure growth assays to 4 0 6 measuring host-microbe interactions, using macrophages as representative 4 0 7 phagocytes. We first hypothesized that the oral strains may show decreased recognition bone-marrow derived macrophages and determining the ratio of internalized to external 4 1 2 cells by differential staining and microscopy ( Fig 4B) [58]. Although most isolates were 4 1 3 not significantly different from the SC5314 reference, the isolates generally had a lower 4 1 4 phagocytic rate than SC5314. Additionally, there was no correlation between sample 4 1 5 origin site or clade with phagocytosis rate. As phagocytosis was not a major differentiating factor between strains, we then 4 1 7 wanted to examine whether the strains would induce different levels of macrophage cell 4 1 8 death. We primed bone-marrow derived macrophage for 2 hours with LPS before 4 1 9 infecting with each of our isolates for 4 hours. Following infection, we stained the cells 4 2 0 with propidium iodide (PI) as a measure of cell death ( Fig 4C). On average, the 4 2 1 commensal isolates induced between 5% and 20% cell death, which was significantly 4 2 2 lower than the reference strain, SC5314, which induced an average of 40% cell death. Several strains, including the 3 aggregating strains, 882-60, 882-46, and 811-7, were 4 2 4 1 9 not significantly different than the mock condition. Other than SC5314, only one isolate, 4 2 5 831-1, was significantly different (p = 0.0411) from the mock condition.

2 6
Recently, we showed that C. albicans mutants that filament in serum are not 4 2 7 always filamentous within macrophages [59]. As filamentation is linked, but not required, those that filamented more than the SC5314 reference strain. Notably, the extent of 4 3 4 filamentation did not correlate with colony morphology or invasion on agar, with many  Using individual phenotypic measures, we were unable to identify associations based on the cosign metric. We observed three major clusters, but they did not isolates showed extensive phenotypic variation, but this was not dependent on the body 4 4 8 site or participant from which they were collected. isolates from each individual, we were able to obtain phenotypically diverse strains with 4 5 6 a limited set of unique SNPs between isolates, allowing us to identify causative variants 4 5 7 associated with a particular phenotype.

5 8
We focused on the strains from donor 814, as this donor's matched oral and these 163 colonies were included in the condensed set, and these 6 strains clustered 4 6 2 tightly in Clade 1, which we hypothesized would allow us to identify causative variants 4 6 3 associated with particular phenotypes that were divergent between strains. were less invasive ( Fig 5A). Moreover, from this donor's 163 isolates, only this single 4 6 7 isolate exhibited the hyper invasive phenotype into Spider agar at 37°C ( Fig 5B); this 4 6 8 phenotype was the motivation for initially including this strain in the sequenced set.   that it is highly correlated with genes involved in regulating the yeast-to-hyphal 4 8 8 morphogenic transition (Fig 5C). To test whether this SNV can drive an invasive 4 8 9 phenotype, we generated complementation plasmids encoding the ZMS1T681S allele  changing amino acid 681 to a serine is a dominant active allele that is sufficient to drive 5 0 7 a hyphal invasion program into Spider agar. We also identified natural variation that was analysis of a limited set of natural isolates from a single host can be exploited to identify  We next examined the fitness and virulence of the commensal isolates relative to 5 1 4 the SC5314 reference strain; we hypothesized that the commensal isolates would have decreased virulence compared to SC5314, a clinical isolate. To test this, we turned to that both strains are equally fit and that the fluorescence does not impose a fitness cost.

2 4
In contrast, most commensal strains had a competitive index > 2, suggesting that these growth exhibited by these strains in many growth conditions (Fig 4A). commensal isolates compared to SC5314 reference. Isolates were competed 5 3 3 against a fluorescent SC5314 isolate, starting at a 1:1 initial inoculum.

3 4
Competitive fitness was calculated as the ratio between fluorescent and non-  Significant differences from the SC5314 reference strain were determined by G. mellonella, comparing the SC5314 reference to 6 isolates from donor 814.

3 9
Each strain was standardized to 2x10 6 cells/mL before inoculating 20 G. Mantel-Cox log-rank test. Asterisks indicate P < 0.05 (*), P < 0.005 (**), P < 5 4 3 0.001 (***) and P < 0.0001 (****) compared with SC5314. The striking increased competitive fitness of the other isolates motivated us to 5 4 6 test whether this increased ratio was correlated with increased disease. Here, we compared with SC5314, to test the hypothesis that increased invasion is associated including the hyper-invasive 814-168 isolate (Fig 6B). Two strains had a slight defect in 5 5 3 virulence (p < 0.05 log-rank test). We additionally tested two other clusters of strains for cause host immune cell death (Fig 4C). Together, this suggests that there is not a we observed wide variation in in vitro host response phenotypes, the isolates generally 5 6 0 2 5 retained their pathogenic potential, indicating that our in vitro assays may not capture 5 6 1 the complex stresses experienced during whole-organism infections. Intra-species analyses of microbial strains can allow for the identification of  variants that was largely absent from previous descriptions of natural diversity. We were 5 8 2 able to leverage clonal variation within a single donor to identify meaningful variation that arose during microevolution. Overall, we performed a systematic phenotypic 5 8 4 analysis of commensal isolates from healthy donors, thus allowing us to examine C.

8 5
albicans genotypic and phenotypic diversity before the transition to virulence.

8 6
Strikingly, our commensal strains generally maintained their capacity to cause 5 8 7 disease, and all strains were able to filament in response to the inducing cues of 10% invade into agar, recent intravital imaging approaches suggest that filamentation in 5 9 2 response to serum matched that seen in vivo more than filamentation in response to all of the commensal isolates suggests that the selective pressures that occur during 5 9 5 mouse models of colonization may not recapitulate the selection that occurs during 5 9 6 human colonization.

9 7
Although we were not able to associate a particular phenotype with increased 5 9 8 systemic disease, we observed extensive phenotypic and genotypic variation between 5 9 9 the commensal isolates. This variability is also consistent with recent work from clinical, 6 0 0 disease-associated strains [18]. Moreover, our commensal strains were able to 6 0 1 proliferate on a range of different environmental conditions, and we did not observe 6 0 2 significant differences in phenotypes between isolates obtained from oral or fecal sites, Previous work has suggested that the C. albicans population within a given 6 0 8 individual is clonal [68,[75][76][77][78], and that the fungus is acquired during birth as a part of 6 0 9 the normal microbiota [42]. In these studies, they were often sampling from patients with 6 1 0 active disease; this may suggest that there is selection for the ability to cause disease, contrast, we identified disparate individuals that appeared to be colonized by strains that 6 1 3 were nearly identical, suggesting that there was some transmission between individuals.

1 4
Whether these transmission events allow for long-term colonization, and how they affect 6 1 5 the initial C. albicans colonizing strains, is still not fully understood. We also observed diversification within hosts, with multiple instances of closely- with co-expression, which we term "limited diversity exploitation", to identify a candidate 6 2 0 transcription factor that regulates invasion. Previous work on a ZMS1 knockout strain of 6 2 1 C. albicans did not show any differences in phenotype compared with the parent strain 6 2 2 [65]. However, we observed that a single amino acid substitution in the predicted fungal 6 2 3 transcription factor regulatory middle homology region was sufficient to drive hyper-6 2 4 invasive growth. Moreover, this phenotype was distinct from the deletion phenotypes in 6 2 5 these two genetic backgrounds, again contrasting with the SC5314 reference strain. It is resistance, and our results suggest that studying more than a single representative 6 2 9 isolate gives opportunities to discover new biology. and HUM00118951) and was conducted in compliance with the Helsinki Declaration. albicans colonies were individually inoculated into fresh 96-well plate in YPD and after 6 4 5 24 hours of incubation at 30° C, 50% glycerol was added to generate the stock plates. For each oral sample, individual Candida albicans colonies were picked from the 6 4 7 CHROMagar plates and incubated overnight in 100 µL of YPD in a 96 well plate. After 6 4 8 24 hours incubation in 30° C, 50% glycerol was added to the 96 wells to generate the 6 4 9 stock plates. All strains were maintained in -80 C cryoculture. Media for growth curves is described in Supplemental Table 1. YPD in 96-well plates at 30° C, then subcultured into fresh media using a sterile pinner. Growth curves were performed on a BioTek 800 TS Absorbance Reader in the  Table S1. Summary statistics from GrowthcurveR for the entire 6 6 1 collection of isolates are included in Table S2. Summary statistics from GrowthcurveR 6 6 2 for the condensed set in multiple environmental conditions are included in Table S4. YPD in 96 well plates at 30° C, then subcultured into 100 μ L of YPD at either 30° C or 6 6 7 42° C for three hours. After three hours, the plates were imaged on an Olympus IX80 6 6 8 microscope at 20X magnification. To test the response to serum, overnight cultures To identify single nucleotide variants (SNVs), we mapped 435 read libraries to to convert alignment files, remove PCR duplicates, and assign read groups [49]. include high-confidence SNVs, we removed low quality variant calls at the (i) individual individual sample genotype calls if their genotype quality (i.e., GQ) was < 0.99 (out of allele (i.e., not diallelic), an alternate allele longer than 1 nucleotide (i.e., indels), or with 8 1 5 >5% missing genotype calls across samples. Finally, we removed entire samples from 8 1 6 the combined VCF if their genotype calls across all loci were more than 90% missing,  To remove redundancy and focus on natural C. albicans diversification, we studies (i.e., [34,51,52] generate an input matrix for distance calculation and NJ clustering, we coded genotypes genotypes (i.e., coded as N) were ignored. Using these raw distances, we calculated generalized distance function for sample i and sample j...
…where nSNPs is the total number of high-quality SNPs in our dataset (i.e., GT ix is the genotype of sample i at locus x, and GT jx is genotype of sample j at locus x.
The resulting distance matrix was clustered with the nj function in phytools in R [83]. We 8 3 9 visualized the resulting tree structure and associated data with ggtree in R [84][85][86]. ) for each sample was calculated using the following formula: calls of a give type which overlapped with each bin. To identify potential translocations from the TELL-seq data, we used the Tell-Link where consecutive aligned segments longer than 10,000 bp on the same contig aligned 8 5 9 to different chromosomes. To experimentally validate the predicted junctions, we i.e., one from each of the two chromosomes predicted to be joined together. Primers were selected from the 500 bp directly upstream and downstream from the predicted used. Primers are in Table S5. To make the ZMS1 allele swap strains, we used the NEBuilder HiFi assembly kit 8 6 9 to clone both alleles of ZMS1 from the 814-168 strain background into the pUC19 presence of specific ZMS1 alleles by Sanger sequencing using oTO736. To generate the deletion strains, the ZMS1::NAT cassette was amplified from the Integration was tested using oTO5 and oTO1218 and loss of the wild-type ZMS1 gene 8 8 2 was tested using oTO736 and oTO1218.


Transcriptional landscape of human microglia implicates age, sex, and APOE‐related immunometabolic pathway perturbations

Abstract Microglia have fundamental roles in health and disease; however, effects of age, sex, and genetic factors on human microglia have not been fully explored. We applied bulk and single‐cell approaches to comprehensively characterize human microglia transcriptomes and their associations with age, sex, and APOE. We identified a novel microglial signature, characterized its expression in bulk tissue and single‐cell microglia transcriptomes. We discovered microglial co‐expression network modules associated with age, sex, and APOE‐ε4 that are enriched for lipid and carbohydrate metabolism genes. Integrated analyses of modules with single‐cell transcriptomes revealed significant overlap between age‐associated module genes and both pro‐inflammatory and disease‐associated microglial clusters. These modules and clusters harbor known neurodegenerative disease genes including APOE, PLCG2, and BIN1. Meta‐analyses with published bulk and single‐cell microglial datasets further supported our findings. Thus, these data represent a well‐characterized human microglial transcriptome resource and highlight age, sex, and APOE‐related microglial immunometabolism perturbations with potential relevance in neurodegeneration.

| INTRODUC TI ON
Microglia are the resident macrophages of the central nervous system (CNS), responsible for clearance of cellular debris and pathological protein aggregates. In the healthy brain, they exist in a homeostatic state and can be induced to a reactive state in response to changes in the CNS microenvironment, such as inflammation and neuronal damage (Masuda et al., 2020). They are fundamental to maintaining brain homeostasis during development, aging, and disease; therefore, microglial dysfunction could ultimately lead to neurodegeneration (Li & Barres, 2018). Microglia are integral to the pathophysiology of neurodegenerative diseases, including Alzheimer's disease (AD) and multiple sclerosis, with chronic inflammation implicated as a contributing factor (Hickman et al., 2018;Keren-Shaul et al., 2017;Krasemann et al., 2017).
Fresh human brain tissue studies are imperative to the characterization of the microglial transcriptome in health and disease; however, accessibility is limited. Although single nuclei studies using frozen tissue provide an easier alternative, recent studies have demonstrated limitations in detecting substantial populations of less abundant cell types (Del-Aguila et al., 2019;Mathys et al., 2019). Additionally, it was recently reported that many microglial activation genes are expressed in the cytosol and therefore are likely to be missed by single nuclei RNA sequencing (snRNAseq; Thrupp et al., 2020). Recent single-cell studies aiming to characterize microglial gene expression using fresh tissue have highlighted the heterogeneity in microglial phenotypes Olah et al., 2020;Sankowski et al., 2019). This has revealed that phenotypic changes are not binary but rather a spectrum of states in which microglia can simultaneously co-exist during transition from homeostatic to more reactive states. Additionally, these different subsets could have specialized functions in brain homeostasis and dysfunction. Thus, it is increasingly important to characterize these heterogeneous subpopulations to understand their roles in health and disease. This could also help facilitate the design of novel therapeutic approaches to target-specific subpopulations of cells and modulate their activity (Li & Barres, 2018).
Obtaining fresh human tissue from neurosurgeries allows us to study the mechanisms of microglial function in living cells.
Unfortunately, this tissue is usually excised from surgical procedures for tumor resection or relieving temporal lobe epilepsy, rendering it difficult to distinguish between normal and disease-affected tissue. Darmanis et al. (2017) investigated the effect of GBM tumors on CNS cell types and surrounding tissue, revealing that myeloid cells are greatly affected by the tumor microenvironment. They found that peri-tumor myeloid populations were primarily pro-inflammatory microglia compared to macrophages within the tumor core (Darmanis et al., 2017). In temporal lobe epilepsy, two distinct microglial phenotypes have been identified with microglia present in sclerotic areas with few neurons expressing markers of activation, including anti-inflammatory cytokine IL10 (Kinoshita & Koyama, 2021;Morin-Brureau et al., 2018). The other phenotype occurs transiently following a seizure, with secretion of interleukins CXCL8 and IL1B mediated by the NLRP3 inflammasome (Morin-Brureau et al., 2018).
Microglial expression has also been shown to be affected by aging (Galatro et al., 2017;Olah et al., 2018); however, few studies have investigated the effects of sex and genetic factors on human microglia. Sex differences in microglia have been previously reported in mice, with females being predisposed to harboring more activated microglia than males (Frigerio et al., 2019;Nelson et al., 2017;Stephen et al., 2019). APOE, a lipoprotein of which the ε4 allele (APOE-ε4) is a major risk factor for AD and also implicated in other neurodegenerative diseases (Yamazaki et al., 2019]), is upregulated in disease-associated microglia (DAM) in mice and humans, but downregulated in astrocyte and oligodendrocyte subpopulations (Grubman et al., 2019;Hammond et al., 2019;Keren-Shaul et al., 2017;Mathys et al., 2019). In microglia and neurons, APOE interacts with LDL receptors to facilitate endocytosis of cholesterol and phospholipids and modulate lipid homeostasis in the brain (Gamache et al., 2020). Such studies provide growing support for cell typespecific functions of APOE; however, its effects on microglia remain to be fully elucidated. Thereby, identifying age, sex, and APOEassociated pathways in microglia will provide greater insight into the functions of specific microglial subsets in relation to these risk factors. Interindividual variability and diversity in functional states makes targeting specific microglial subsets in disease challenging for modulating these cells (Li & Barres, 2018). Identifying the mechanisms regulating microglial homeostasis and activation can allow us to manipulate these cells for therapeutic purposes.
In this study, we leveraged both bulk and single-cell approaches to provide a comprehensive characterization of the adult human microglial transcriptome. We obtained fresh intraoperative neurosur-| 3 of 21 PATEL ET AL.

| RE SULTS
To uncover microglial transcriptional profiles and their associations with age, sex, and APOE, we performed microglial cell typespecific and single-cell RNA sequencing (scRNAseq) studies in fresh human brain tissue. We obtained neurosurgical tissue unaffected by the primary disease process from 19 human donors ( Figure S1). Microglia were isolated by CD11b + microbead selection followed by FACS sorting of cells expressing the CD11b + / CD45 intermediate microglial signature to acquire a more purified population. These samples underwent bulk microglia RNAseq, with subsets of these and additional samples also undergoing 10x scRNAseq (n = 5) and bulk tissue RNAseq (n = 9; Figure 1a; Table 1). Validation of sorted microglia using qPCR showed the expected CD11b + /CD45 intermediate /P2RY12 + microglial signature (Li & Barres, 2018) with no expression of other cell type markers, indicating that we isolated a highly enriched microglial population ( Figure S2).

F I G U R E 1
Characterization of our core human microglial signature. (a) Schematic illustrating our experimental approach for isolating microglial populations from fresh brain tissue and data analyses. [Created with BioRender.com] (b) MSigDB GO terms enriched in our microglial signature genes and top 25 genes for each. (c) Venn diagram showing number of overlapping genes between our microglial signature and those previously reported from Galatro et al. (2017), Gosselin et al. (2017) and Olah et al. (2018) To define a core human microglial signature, we calculated log 2 fold change and q-values of differential expression for each gene between bulk microglia RNAseq data in our study and bulk brain RNAseq data from 7 AMP-AD datasets provided by Mayo Clinic (Allen et al., 2016]), Mount Sinai Brain Bank ,  (Table S7). GO enrichment using MSigDB showed that this signature was enriched for genes involved in immune-related and inflammatory response pathways as would be expected, and leukocyte-mediated immunity ( Figure 1b).
To determine the ability of bulk brain tissue data to capture microglial genes, we assessed the expression levels of our microglial signature genes in each of the 7 AMP-AD bulk brain RNAseq datasets. Of the 1971 microglial signature genes in our study, 37-47% were captured in these bulk brain datasets ( Figure S3A-B). Our microglial signature genes comprised 3.6-4.5% of the expressed bulk brain transcriptome, consistent with prior estimations (Mathys et al., 2019;Wang et al., 2020). We next compared bulk microglia RNAseq transcript levels to that obtained from bulk tissue RNAseq of neurosurgical fresh brain tissue samples. Bulk fresh brain tissue does not capture all microglial marker genes, as demonstrated by the low correlation between bulk tissue and bulk microglia data (R = 0.46, Table S6). This reiterates the need for complementary single-cell type data to deconvolute cell typespecific expression. We provide the list of microglial signature genes that are also expressed at high levels in bulk brain tissue data (Table   S5), which can serve as a validated resource for microglial signature gene markers in bulk RNAseq datasets.
To determine how the microglial signature in this study compared where 49.7% of their genes were also present in our signature, and 22% of ours in their signature. Gosselin et al. (2017) samples were also obtained from neurosurgical tissue resections like our cohort and are unlike Galatro et al. (2017) and Olah et al. (2018) samples that were harvested during autopsy. Although there appears to be a common set of microglial genes consistent across signatures, each also harbors many unique genes, which could be due to study or individual specific differences.
2.2 | Transcriptional profiling of microglia discovers co-expression networks and implicates lipid and carbohydrate metabolism pathways associated with age, sex, and APOE We generated gene co-expression networks using WGCNA (Langfelder & Horvath, 2008) to reduce number of tests and increase power to detect genetic associations with age, sex, and APOE.
We identified 7 modules with significant associations (Figure 2; Figure S5;   Rangaraju et al., 2018b), CTSL, (Cermak et al., 2016) and UNC5B (Ahn et al., 2020;Xu et al., 2016). Due to the nature of the tissue, we compared expression levels of the key module genes within tumor and epilepsy samples ( Figure S6). We observed significant differences due to diagnosis in NPC2, PLAU, APOC1, and IKBKE; therefore, it is important to note that some of their associations may be confounded due to the disease state.

| Single-cell transcriptome reveals specific subtypes of microglia
To uncover distinct microglial subtypes, a subset of sorted microglial samples from neurosurgical brain tissue underwent single-cell expression profiling. We obtained 26,558 cells from 5 unique individuals, including one individual who underwent epilepsy surgery and had samples from two brain regions (Table 1). Analysis of the scRNAseq data from these samples revealed 13 distinct cell clusters, which were annotated using established neuronal and glial marker genes from the literature ( Table S11). For these samples, the most marked difference was observed for macrophages (cluster 9) and homeostatic microglia (cluster 2), which had greater contributions from the mesiotemporal and anterior temporal regions, respectively. This could be due to the proximity of the mesiotemporal sample to the disease-affected region.
We characterized the microglial clusters by their expression of es-  Figure S8; Table S12). Most of these markers are distinct to a single cluster, although some clusters appeared to have similarities in their marker expressions. To define the proximity of their transcriptional profiles, we performed hierarchical clustering of the microglial clusters ( Figure 3d). We determined that the homeostatic microglia cluster 2 was transcriptionally closest to clusters 7 and 11, which may represent subtypes of homeostatic microglia. Cluster 11 is enriched for markers of cell proliferation (STMN1, H2AFZ, PCNA, and MKI67), some of which were also observed by Olah et al. (2020), suggesting that these could be proliferating microglia. Clusters 1 and 6 both expressed inflammatory chemokines CCL2 and CCL4 and antiinflammatory molecule EGR2; however, cluster 6 was more closely related to DAM, whereas cluster 1 (named as Leukocyte-recruiting cluster) represented a more pro-inflammatory signature with greater expression of IL6 and TNFα. Cluster 6 (named as interferon-response cluster) also highly expressed interferon-related marker IFITM3 and ISG15, also observed in a cluster by Olah et al. (2020), which they defined as an interferon response-enriched subset. The upregulation of chemokines and interleukins in these clusters suggest that they could be involved in recruitment of other immune cells. These findings highlight different transcriptional profiles for these two inflammatory clusters that may represent distinct activated microglia subtypes. Cluster 3 highly expressed heat shock protein HSPA1A, an immediate early gene (Schmunk et al., 2020) reportedly involved in antigen processing (Aung et al., 2012), response to stress and injury and exhibiting decreased gene expression in multiple sclerosis patients (Gandhi et al., 2010;Satoh et al., 2005). Several were upregulated in this cluster, suggesting that this may represent cells that underwent dissociation-induced stress .
Several of the clusters did not express well known existing cell type markers. Clusters 5/8 and FOS + 0/4 were transcriptionally closest to one another (Figure 3d). Cluster 5 has distinct expression of immunoreactive marker CD163, which was not observed in other subsets except macrophages. Several HLA genes are also highly expressed in this cluster, suggesting that these may be immunoreactive microglia (Hendrickx et al., 2017). Cluster 8 marker FTL has recently been used to characterize iron-accumulating microglia (Kenkhuis et al., 2021). Our findings highlight transcriptional profiles for known microglial clusters, describe the transcriptional proximity of these clusters and suggest that less well-defined clusters could potentially represent novel or intermediate transcriptional states of microglia.
Our microglial signature was significantly enriched in more clusters expressing more activated markers (Table S13) full enrichment statistics provided in Table S15). Age-associated co-expression network ME14, implicated in lipid metabolism, was significantly enriched in interferon-response (cluster 6) and DAM (cluster 10) clusters. Genes within module 28, which was significantly upregulated with APOE-ε4, had statistically significant enrichment in all clusters except cluster 7. There was no statistically significant enrichment for any of the other microglial modules that had significant age, sex, or APOE associations, suggesting that these factors may have ubiquitous effects on most microglial subtypes. Some of the remaining microglial co-expression networks had distinct patterns of cluster enrichment ( Figure S9), suggesting that some but not all networks could be representative of distinct microglial subtypes.

| Meta-analyses with published datasets support age, sex, and APOE associations
Each dataset was individually processed through the same MAPR-seq pipeline for quality control to minimize variability due to data processing. Meta-analysis of WGCNA results was performed by coercing the external dataset co-expression networks onto our existing co-expression networks. The forest plot in Figure 4a highlights the individual and combined associations for networks of interest across the datasets where age, sex, or APOE information was available (Table   S17). For module ME14 associations with age, Galatro et al. (2017) also exhibited a similar direction of effect (R = 0.30, p = 0.06), whereas Srinivasan et al. (2020) did not show any association (R = −0.003, p = 0.99), likely due to a higher median age of individuals in the study.
When meta-analyzed, sex-associated ME26 genes were significantly downregulated in males in all datasets, supporting our finding. This module was also significantly associated with APOE e4 carriers in the Srinivasan dataset (R = 0.42, p = 0.04). However, Srinivasan samples were inversely correlated with APOE for ME23 in comparison to our data (R = 0.61, p = 0.07). Srinivasan et al. (2020) was inversely correlated with age and APOE for ME23, but not with sex or APOE for ME26. Overall, it appears that the direction of effects was similar for correlation with these traits in most datasets ( Figure S10).  (Table S18). This is similar to what we observed in our single-cell data (Table S13), supporting the notion that our microglial signature is enriched for activated cells.
In this study, we sought to overcome these knowledge gaps by characterizing the transcriptome of sorted bulk and single-cell microglial populations isolated from fresh human brain tissue. We iden- Our findings in sorted cells from fresh human brain tissue provide transcriptional evidence for immunometabolism changes and proinflammatory phenotype with microglial aging, thereby contributing essential complementary data from humans for this cell type.
Besides module ME14, we determined that ME26 is also enriched for lipid metabolism genes. ME26 module expression is higher in both APOE-ε4 and female sex; however, we note that in our sorted bulk microglia RNAseq samples, there were no male APOE-ε4 carriers.
Therefore, the distinct influence of sex and APOE on the expression of this module remains to be established. APOE-ε4, a major risk factor for Alzheimer's disease, has the lowest lipid binding efficiency compared with other APOE isoforms (Chausse et al., 2020). Increased cholesterol accumulation has been reported in both iPSC-driven astrocytes from APOE-ε4 carriers (Lin et al., 2018) and in Apoe-deficient microglia (Nugent et al., 2020). These findings collectively support a role for APOE-ε4 associated microglial transcriptional changes and disrupted cholesterol metabolism. Using our sorted microglia RNAseq data, we identified five additional modules that associate with APOE-ε4, one in a positive direction (ME28) and four negatively (ME4, ME23, ME34, and ME36). Of these, module ME23 had the second most significant APOE-ε4 association after ME26. Interestingly, ME23 was enriched for carbohydrate metabolism biological processes, which are also tightly regulated in microglia (Bernier et al., 2020). Module ME23 harbors known AD risk genes BIN1 and PLCG2, where the latter is a microglial gene that modulates signaling through TREM2  and also a hub gene in this module. ME23 genes BIN1, JUN, and TGFBR2 were found to be reduced in a mouse microglial neurodegenerative phenotype gene signature (Krasemann et al., 2017). These findings further demonstrate the consistency of our human microglial data with that from mouse models and supports perturbed microglial immunometabolism as a potential pathogenic mechanism in neurode-  (Table   S11). As there were only 5 cells in this cluster, this is likely to denote contamination from the sorting procedures. We identified microglial clusters that share characteristics of those previously reported in mice (Keren-Shaul et al., 2017) and humans Olah et al., 2020), such as DAM. We also uncovered clusters that had not been previously characterized in the literature, including cluster 7, exhibiting high expression levels of astrocytic marker gene SLC1A3. Microglial expression of SLC1A3 was previously shown to occur in mice and humans especially in disease states (Chrétien et al., 2004;Grassivaro et al., 2020;Wilhelmsson et al., 2017). We also leveraged these scRNAseq data to further characterize the sorted bulk microglial expression modules. Integration of our samples with Olah et al. allowed us to explore microglial subtypes at single-cell resolution with increased power. We found that even in this larger dataset, our microglial signature was similarly enriched in the more activated subtype clusters, demonstrating the robustness of this signature across datasets. The homogeneity in clustering highlighted shared expression patterns between cells from both datasets, providing further support for the putative microglial subtypes we identified here. Hence, our microglial scRNAseq data contribute further to the emerging single-cell landscape of this cell type.
Activated microglia are a hallmark in both brain cancers and temporal lobe epilepsy in response to the inflammation (Darmanis et al., 2017;Kaminska et al., 2021;Kinoshita & Koyama, 2021;Morin-Brureau et al., 2018;Ochocka et al., 2021). We acknowledge that the tissue used in this study is sourced from tumor or epileptogenic tissue adjacent regions in individuals with diagnoses of brain tumors or epilepsy, meaning it is not entirely healthy. Therefore, it is possible that the activated subtypes we identify here may be due to the nature of the tissue. Additionally, sex-specific expression differences have been observed in glioma-activated microglia . Nonetheless, we did not observe significant differences in most genes we highlighted here due to diagnosis ( Figure S5). There is also a large cluster of homeostatic microglia present in these samples, indicating that not all cells are activated. While the focus of our work is AD, these findings also have potential relevance in the fields of cancers, epilepsy, and other diseases.
We recognize that our study has several limitations, primarily owing to the difficulty in obtaining high-quality neurosurgical brain tissue, which leads to limited sample size and variability in tissue, diagnoses, and patient demographics. Even though we have utilized tissue surgically separated from disease tissue, the samples are from epilepsy and various brain tumor patients representing multiple diagnoses. We isolated microglia using an approach which should minimize activation; however, we cannot definitively rule out stress-induced transcriptomic changes during isolation. Despite these caveats, we could identify microglial co-expression modules and subclusters with multiple features that are consistent with prior publications from model systems (Hammond et al., 2019;Keren-Shaul et al., 2017;Krasemann et al., 2017;Marschallinger et al., 2020). Our scRNAseq clusters have contributions from both tumor and epilepsy samples, suggesting that our findings are unlikely to be driven by any one diagnoses. Furthermore, there are few published studies using fresh brain tissue to study microglia and thus our integrated single-cell data showing a homogenous cluster of microglia highlights the robustness of the methodology.
In summary, our study on sorted bulk microglia RNAseq and scRNAseq from fresh brain tissue yield several key findings. We identify a microglial gene signature from sorted bulk microglia, characterize its expression in bulk brain RNAseq across 7 datasets comprising 6 regions, in bulk fresh brain RNAseq and in microglial scRNAseq

| Patient samples
Fresh human brain tissue was obtained from patients undergoing neurosurgical procedures for epilepsy or tumor resection. Tissues determined to be grossly unaffected by the primary disease process were utilized for the present study ( Figure 1). Patient samples were transported from the operating room to the laboratory in 1X DPBS (Thermo Fisher; 14287080) for processing within 1-2 h of resection.
Human tissue was collected with informed consent prior to surgery and all procedures were approved by the Mayo Clinic Institutional Review Board and are HIPAA compliant.

| Tissue dissociation
Tissue was dissected to remove necrotic tissue, white matter and excess vascular tissue, to retain only cortical gray matter. The remaining tissue was cut into sagittal slices and weighed before being processed using the Adult Brain Dissociation Kit (Miltenyi; 130-107-677) as per the manufacturer's protocol. Debris removal (Miltenyi; 130-109-398) and red blood cell lysis (Miltenyi; 130-094-183) were also performed. All procedures were carried out on ice. The resulting homogenate was filtered through a 70µm filter before proceeding.

| Magnetic-activated cell sorting (MACS)
The cell suspension was first enriched for CD11b + cells by incubat-

| Identification of a core microglial signature from bulk microglia data
To define a core microglial signature, we compared our bulk microglia data to cognitively normal control samples from the AMP-AD bulk tissue transcriptome data from 7 different datasets representing 6 Genes that passed these criteria and were significant in comparisons with all 7 bulk brain datasets determined the microglial signature.
These signature genes were assessed for GO term enrichment with biological pathways using MsigDB. REViGO (Supek et al., 2011) tree plots were generated in R using GO terms obtained from MsigDB.

| Weighted gene co-expression network analysis
The CQN normalized expression values from bulk microglia were input to R WGCNA (Langfelder & Horvath, 2008)

| Over-representation and correlation analyses
Hypergeometric testing was performed in R to determine the enrichment of a select set of genes in previously reported signatures, bulk tissue expressed genes, WGCNA modules or 10X single-cell clusters. Correlation between bulk tissue and bulk microglial normalized CQN data was calculated using Spearman's rank correlation.
Concordant and discordantly correlated genes were determined using the upper and lower quartiles from each dataset. Quality control, normalization, clustering, and marker gene identification were performed with Seurat v3 (Stuart et al., 2019), followed by annotation of clusters using established cell type markers. We kept

| Meta-analysis of bulk microglia RNAseq datasets
We obtained sets of co-expression genes, that is, WGCNA modules from our own data and identified several modules whose eigengenes were correlated with age/sex/APOE. To check whether these correlations hold for external datasets, we downloaded raw reads in FASTQ format for three external bulk microglia datasets listed in Table S17. Reads were mapped to human genome build hg38 and were counted and normalized in the same fashion as described previously. For each WGCNA module, we identified the central genes (i.e., genes whose Pearson correlation with module eigengene > 0.75). Using these central genes, module eigengenes were calculated in all datasets-ours and three external ones. We correlated module eigengenes with traits. Finally, we performed meta-analysis to combine correlations from multiple datasets using metacor function (random effect model) in R meta package.

| Meta-analysis of single-cell RNAseq datasets
Raw single-cell RNAseq data from Olah et al. (2020) was downloaded through Synapse (syn21438358) and processed through Cell Ranger. Quality control was performed as described prior to integration with our samples using Seurat v4.0.4. Integration was performed to combine the datasets by individual rather than sample. A total of 9 samples and 15,819 cells were retained from the Olah data.
Cells were annotated to include dataset of origin. Hypergeometric tests were performed to determine the enrichment of our microglial signature within each integrated cluster. Additionally, we also looked at overlap between integrated cluster genes and marker genes from our original single-cell dataset. To identify where our original cells localized within the integrated dataset, we mapped the original cell IDs to the integrated clusters and calculated percentage overlap.

ACK N OWLED G M ENTS
The authors thank the patients and their families for their participa- The results published here are in whole or in part based on data ob- Eric Schadt from Mount Sinai School of Medicine.

CO N FLI C T O F I NTE R E S T
None.

AUTH O R CO NTR I B UTI O N S
TP and NET wrote the manuscript; NET and MA designed the study; TP, XW, and ZQ performed data analysis; JC consulted on statistical methods; TP, TPC, XW, YM, and RMA generated tables and figures; EM, CAG, SG, KC, RW, HGC, and AQH provided neurosurgical tissue samples; TP, TPC, LJLT, SJL, SL, FQTN, CCGH, KGM, and TN performed experimental procedures from blood and tissue samples. All authors read the manuscript and provided input and consultation. NET oversaw the study and provided direction, funding, and resources.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data in this manuscript are available via the AD Knowledge Portal